文件名称:batbp
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- 上传时间：2012-11-16
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Batch version of the back-propagation algorithm.
％ Given a set of corresponding input-output pairs and an initial network
％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the
％ network with backpropagation.
％
％ The activation functions must be either linear or tanh. The network
％ architecture is defined by the matrix NetDef consisting of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
％-Batch version of the back-propagation algorithm. Given a set of corresponding input-output pairs and an initial network [W1, W2, critvec, iter] = batbp (NetDef, W1, W2, PHI, Y, trparms) trains the network with backpropagation. The activation functions must be either linear or tanh. The network architecture is defined by the matrix NetDef consisting of two rows. The first row specifies the hidden layer while the second specifies the output layer.
％ Given a set of corresponding input-output pairs and an initial network
％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the
％ network with backpropagation.
％
％ The activation functions must be either linear or tanh. The network
％ architecture is defined by the matrix NetDef consisting of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
％-Batch version of the back-propagation algorithm. Given a set of corresponding input-output pairs and an initial network [W1, W2, critvec, iter] = batbp (NetDef, W1, W2, PHI, Y, trparms) trains the network with backpropagation. The activation functions must be either linear or tanh. The network architecture is defined by the matrix NetDef consisting of two rows. The first row specifies the hidden layer while the second specifies the output layer.
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